Regional power planning method and system under carbon constraint

Abstract

The invention provides a regional power planning method and system under carbon constraint, and the method comprises the steps: constructing a regional carbon emission model based on regional historical thermal power generation amount, the proportion of electric energy to terminal energy consumption, and non-power index data highly related to carbon emission; and then, dynamically predicting the development trend of non-power indexes highly related to carbon emission, constructing a regional power planning model based on the regional carbon emission model and the dynamic prediction result of the non-power indexes in combination with carbon constraint, and finally, solving the regional power planning model. And obtaining a regional power planning scheme including regional thermal power generation amount and power consumption amount. According to the method, power planning is served by analyzing carbon emission driving factors and combining a regional carbon emission model from the perspective of carbon, a safe-economic-low-carbon novel power system can be constructed, and low-carbon transformation of a regional power grid is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power, and particularly relates to a regional electric power planning method and system under carbon constraint. Background Art

[0002] With the intensification of global climate change, the power industry, as the main source of carbon emissions, its low-carbon transformation has become an important path to achieve climate governance. From the "carbon perspective", studying the grid planning method to balance the triple goals of security - economy - low carbon is not only an urgent need to address climate change, but also a strategic measure to promote the clean energy structure and ensure energy security, and has become the focus of common concern in the academic and industrial circles.

[0003] The existing research methods for power system planning mainly optimize the power source structure, operation strategies, etc. with security - economy as the orientation, without taking carbon emissions as a rigid constraint condition, and the support for the construction of a new power system is insufficient. At the same time, the existing regional carbon emission models are not only difficult to process small - sample, high - uncertainty non - power index data, but also mostly ignore the dynamic impact of power indexes such as thermal power generation on carbon emissions, and there are limitations in the application in grid planning, which is not conducive to promoting the low - carbon transformation of regional power grids. Summary of the Invention

[0004] The purpose of the present invention is to provide a regional electric power planning method and system under carbon constraint for the above - mentioned problems existing in the prior art.

[0005] To achieve the above purpose, the technical solution of the present invention is as follows:

[0006] In the first aspect, the present invention proposes a regional electric power planning method under carbon constraint, including:

[0007] S1. Based on the regional historical carbon emissions, thermal power generation, the proportion of electric energy in terminal energy consumption, and non - power index data highly correlated with carbon emissions, construct a regional carbon emission model; dynamically predict the development trend of non - power indexes highly correlated with carbon emissions;

[0008] S2. Based on the regional carbon emission model and the dynamic prediction results of non - power indexes, combined with carbon constraints, construct a regional electric power planning model;

[0009] S3. Solve the regional electric power planning model to obtain a regional electric power planning scheme including regional thermal power generation and electricity consumption.

[0010] In the above - mentioned S1, the regional carbon emission model is:

[0011]

[0012] Y1 = {y1(1), y1(2),..., y1(i),..., y1(n)}

[0013] Y2 = {y2(1), y2(2),..., y2(i),..., y2(n)}

[0014] In the above formula, C pre is the regional historical carbon emissions, is the historical data sequence of the i-th non-electricity index highly correlated with carbon emissions. Y1 and Y2 are the regional thermal power generation sequence and the sequence of the proportion of regional electric energy in terminal energy consumption respectively. y1(i) and y2(i) are the regional thermal power generation and the proportion of regional electric energy in terminal energy consumption in the i-th historical year. α i is the elasticity coefficient of the i-th non-electricity index highly correlated with carbon emissions. α m+1 , α m+2 are the elasticity coefficients of regional thermal power generation and the proportion of regional electric energy in terminal energy consumption respectively. α0 is the constant term. m and n are the number of non-electricity indexes highly correlated with carbon emissions and the number of historical years respectively.

[0015] In S2, the objective function of the regional power planning model includes:

[0016]

[0017] In the above formula, y1 and y2 are the regional thermal power generation and the proportion of regional electric energy in terminal energy consumption respectively. Q total is the regional electricity consumption. C is the predicted value of regional carbon emissions, is the predicted value of the i-th non-electricity index highly correlated with carbon emissions. E energy is the total regional terminal energy consumption. κ is the electricity conversion standard coal coefficient;

[0018] The constraint conditions include:

[0019] Carbon constraint:

[0020] C base ≤ C ≤ C target

[0021] In the above formula, C base , C target are the regional basic carbon emissions and the regional carbon emissions target value respectively;

[0022] Thermal power generation constraint:

[0023] y min ≤ y1 ≤ y max

[0024] In the above formula, y min , ymax are the regional baseline load demand and the regional maximum thermal power generation respectively;

[0025] Consumption ratio constraint:

[0026]

[0027] In the above formula, y 2,min and y 2,max are the minimum electricity proportion in the terminal energy consumption of the region and the maximum electricity proportion that can be achieved due to technical limitations in the region respectively, and Q total,min is the minimum electricity demand of the region.

[0028] In the said S1, dynamically predicting the development trend of non-electricity indicators highly related to carbon emissions includes:

[0029] S11. Based on the historical data of non-electricity indicators highly related to carbon emissions, respectively obtain the baseline prediction values of each non-electricity indicator through grey prediction, and generate the dynamic prediction intervals of each non-electricity indicator through Monte Carlo model simulation;

[0030] S12. Accumulate the baseline prediction values of each non-electricity indicator and the corresponding dynamic prediction intervals to obtain the dynamic prediction results of each non-electricity indicator:

[0031]

[0032] In the above formula, is the dynamic prediction value of the i-th non-electricity indicator highly related to carbon emissions in the (n + 1)-th year, is the baseline prediction value of the i-th non-electricity indicator highly related to carbon emissions in the (n + 1)-th year, is the dynamic prediction interval of the i-th non-electricity indicator highly related to carbon emissions.

[0033] In the said S11, generating the dynamic prediction intervals of each non-electricity indicator through Monte Carlo model simulation includes:

[0034] S111. Based on the historical data of each non-electricity indicator, select its probability distribution;

[0035] S112. Use a random number generator to generate random samples that follow the probability distribution of the non-electricity indicator, and generate N groups of random numbers;

[0036] S113. Monte Carlo simulation generates the dynamic prediction normal distribution intervals of each non-electricity indicator.

[0037] Second aspect, the present invention proposes a regional power planning system under carbon constraints, including a regional carbon emission model construction module, a non-electricity indicator dynamic prediction module, a regional power planning model construction module, and a regional power planning model solution module;

[0038] The regional carbon emission model construction module is used to construct a regional carbon emission model based on regional historical carbon emissions, thermal power generation, the proportion of electricity in terminal energy consumption, and non-electricity indicator data highly related to high carbon emissions;

[0039] The non-electricity indicator dynamic prediction module is used to dynamically predict the development trend of non-electricity indicators highly related to high carbon emissions;

[0040] The regional power planning model construction module is used to construct a regional power planning model based on the regional carbon emission model and the dynamic prediction results of non-electricity indicators, in combination with carbon constraints;

[0041] The regional power planning model solution module is used to solve the regional power planning model to obtain a regional power planning scheme including regional thermal power generation and electricity consumption.

[0042] The regional carbon emission model is:

[0043]

[0044] Y1 = {y1(1), y1(2),..., y1(i),..., y1(n)}

[0045] Y2 = {y2(1), y2(2),..., y2(i),..., y2(n)}

[0046] In the above formula, C pre is the regional historical carbon emissions, is the historical data sequence of the i-th non-electricity indicator highly related to high carbon emissions, Y1 and Y2 are the regional thermal power generation sequence and the regional proportion of electricity in terminal energy consumption sequence respectively, y1(i) and y2(i) are the regional thermal power generation and the regional proportion of electricity in terminal energy consumption in the i-th historical year respectively, α i is the elasticity coefficient of the i-th non-electricity indicator highly related to high carbon emissions, α m+1 and α m+2 are the elasticity coefficients of regional thermal power generation and regional proportion of electricity in terminal energy consumption respectively, α0 is a constant term, m and n are the number of non-electricity indicators highly related to high carbon emissions and the number of historical years respectively.

[0047] The objective function of the regional power planning model includes:

[0048]

[0049] In the above formula, y1 and y2 are the regional thermal power generation and the proportion of regional electric energy in terminal energy consumption respectively, Q total is the regional electricity consumption, C is the predicted value of regional carbon emissions, is the predicted value of the i-th non-electricity index highly correlated with carbon emissions, E energy is the total amount of regional terminal energy consumption, and κ is the coefficient of converting electricity into standard coal;

[0050] The constraint conditions include:

[0051] Carbon constraint:

[0052] C base ≤C≤C target

[0053] In the above formula, C base and C target are the most basic carbon emissions and the target value of regional carbon emissions respectively;

[0054] Thermal power generation constraint:

[0055] y min ≤y1≤y max

[0056] In the above formula, y min and y max are the regional benchmark load demand and the maximum thermal power generation respectively;

[0057] Consumption proportion constraint:

[0058]

[0059] In the above formula, y 2,min and y 2,max are the minimum proportion of electric energy in terminal energy consumption and the maximum proportion of electric energy in terminal energy consumption that can be achieved due to technology limitations respectively, Q total,min is the minimum electricity demand of the region.

[0060] The non-electricity index dynamic prediction module includes a grey prediction unit, a Monte Carlo model unit, and an accumulation unit;

[0061] The grey prediction unit is used to obtain the benchmark prediction values of each non-electricity index through grey prediction based on the historical data of the non-electricity indexes highly correlated with carbon emissions;

[0062] The Monte Carlo model unit is used to simulate and generate the dynamic prediction intervals of each non-electricity index through the Monte Carlo model based on the historical data of the non-electricity indexes highly correlated with carbon emissions;

[0063] The accumulation unit is used to accumulate the benchmark prediction values of each non - power index and the corresponding dynamic prediction intervals to obtain the dynamic prediction results of each non - power index:

[0064]

[0065] In the above formula, is the dynamic prediction value of the i - th non - power index highly correlated with carbon emissions in the (n + 1)-th year, is the benchmark prediction value of the i - th non - power index highly correlated with carbon emissions in the (n + 1)-th year, is the dynamic prediction interval of the i - th non - power index highly correlated with carbon emissions.

[0066] The Monte Carlo model unit adopts the following strategy to generate the dynamic prediction intervals of each non - power index:

[0067] A1. Based on the historical data of each non - power index, select its probability distribution;

[0068] A2. Use a random number generator to generate random samples that follow the probability distribution of the non - power index, and generate N groups of random numbers;

[0069] A3. Monte Carlo simulation generates the dynamic prediction normal distribution intervals of each non - power index.

[0070] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0071] 1. A regional power planning method under carbon constraints according to the present invention first constructs a regional carbon emission model based on the regional historical thermal power generation, the proportion of electric energy in terminal energy consumption, and the data of non - power indexes highly correlated with high carbon emissions, then dynamically predicts the development trend of non - power indexes highly correlated with high carbon emissions, and then constructs a regional power planning model based on the regional carbon emission model and the dynamic prediction results of non - power indexes, combined with carbon constraints. Finally, the regional power planning model is solved to obtain a regional power planning scheme including regional thermal power generation and electricity consumption. This method starts from the carbon perspective, analyzes the carbon emission driving factors, and combines the regional carbon emission model to serve power planning, which helps to construct a new type of power system that is safe, economic, and low - carbon, and promotes the low - carbon transformation of the regional power grid.

[0072] 2. The regional power planning method under carbon constraint of the present invention uses a grey prediction-Monte Carlo model to conduct dynamic prediction on the development trend of non-power indicators, including obtaining the benchmark prediction values of each non-power indicator based on grey prediction, generating the dynamic prediction intervals of each non-power indicator through the Monte Carlo model, and adding the two. This method effectively combines the advantages of grey prediction, which is suitable for small samples and data with strong trends and has high computational efficiency, and the advantages of Monte Carlo simulation in quantifying uncertainty and avoiding the limitations of single prediction, thus taking into account both trend and randomness, improving the prediction robustness, and being applicable to low-carbon planning of complex systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a flowchart of the method described in Embodiment 1.

[0074] Figure 2 It is a fitting effect diagram of the regional carbon emission model constructed in Embodiment 1.

[0075] Figure 3 It is a structure diagram of the system described in Embodiment 2. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0076] The present invention will be further described in detail below with reference to the drawings and the detailed description of the embodiments.

[0077] Embodiment 1:

[0078] A regional power planning method under carbon constraint, as Figure 1 shown, the specific steps are as follows:

[0079] 1. Collect historical data on the consumption of various types of energy in the region, and combine the power import information to obtain the regional carbon emissions:

[0080]

[0081] In the above formula, C n is the regional carbon emissions in the nth year, λ k , are the carbon emission factors of the kth type of energy and the fth type of imported electricity respectively, G n,k , are the physical quantity and raw material energy consumption of the kth type of energy consumed in the region in the nth year respectively, is the fth type of imported electricity in the region in the nth year, and K and F are the number of energy types consumed in the region and the number of types of imported electricity respectively.

[0082] The carbon emission factors of typical fossil fuels and electricity types are shown in Table 1:

[0083] Table 1 Carbon emission factors of typical fossil fuels and electricity types

[0084] Fossil fuel type Carbon emission factor (kg CO₂ / kg) Raw coal 1.9003 Diesel 3.0959 Gasoline 2.9251 Electricity type Carbon emission factor (kg CO₂ / kWh) Coal-fired power 0.853 Gas-fired power 0.405 .

[0085] 2. Select six candidate non - power indicators, namely population, per capita GDP, energy consumption intensity, consumer price index, urbanization rate, and the proportion of the added value of the secondary industry in GDP, and screen out the non - power indicators that have the most significant impact on carbon emissions, i.e., highly correlated non - power indicators, through calculating the correlation degree between carbon emissions and each candidate non - power indicator. Specifically, it includes:

[0086] 2.1. Take the regional carbon emission sequence as the target variable, and set C n as x0(n), and the reference sequence X0 = {x0(1), x0(2),..., x0(n)}; based on the historical data of the candidate non - power indicators, set the comparison sequence. When the data of the i - th non - power indicator in the n - th year is x i (n), the comparison sequence Xi i of the i - th non - power indicator is Xi i = {x i (1), x i (2),..., x

[0087] 2.2. Perform mean - value processing on the reference sequence and the comparison sequence to eliminate the dimension difference. After processing the data of the j - th year, we have:

[0088]

[0089] 2.3. Based on the local correlation degree between the comparison sequence and the reference sequence for each year, obtain the comprehensive correlation degree γi between the i - th non - power indicator and carbon emissions, as follows:

[0090]

[0091] In the above formula, ρ is the resolution coefficient.

[0092] 2.4. Sort according to the comprehensive correlation degree γ i and screen out 4 non - power indicators that have the most significant impact on carbon emissions and whose comprehensive correlation degree is greater than 0.7. If 4 non - power indicators cannot be screened out, add candidate non - power indicators and repeat 2.1 - 2.4 until 4 non - power indicators are screened out.

[0093] The comprehensive correlation degrees of each non - power indicator calculated in this embodiment are shown in Table 2:

[0094] Table 2 Comprehensive correlation degrees of each non - power indicator

[0095] Index name Comprehensive correlation degree Population quantity 0.72 Per capita GDP 0.87 Consumer price index 0.63 Energy consumption intensity 0.93 Urbanization rate 0.75 Proportion of the added value of the secondary industry in GDP 0.85 .

[0096] 3. Based on the regional historical carbon emissions, thermal power generation, the proportion of electricity in terminal energy consumption, and non-electricity indicator data highly correlated with high carbon emissions, using the STIRPAT extended model, construct the following regional carbon emission model:

[0097]

[0098] Y1 = {y1(1), y1(2),..., y1(i),..., y1(n)}

[0099] Y2 = {y2(1), y2(2),..., y2(i),..., y2(n)}

[0100] In the above formula, C pre is the regional historical carbon emissions, is the historical data sequence of the i-th non-electricity indicator highly correlated with high carbon emissions. Y1 and Y2 are the regional thermal power generation sequence and the sequence of the proportion of electricity in terminal energy consumption respectively. y1(i) and y2(i) are the regional thermal power generation and the proportion of electricity in terminal energy consumption in the i-th historical year respectively. α i , α m+1 , α m+2 are elasticity coefficients, α0 is a constant term, m and n are the number of non-electricity indicators highly correlated with high carbon emissions and the number of historical years respectively.

[0101] The correlation R 2 of the regional carbon emission model constructed in this embodiment reaches 0.947, and the maximum deviation from 2013 to 2023 is 2.28%. The fitting performance is as Figure 2 shown.

[0102] 4. Use the grey prediction-Monte Carlo model to dynamically predict the development trends of the 4 non-electricity indicators screened and collected that are highly correlated with high carbon emissions, including:

[0103] 4.1. Preprocess the historical data of each non-electricity indicator highly correlated with high carbon emissions to ensure that the original data sequence is a non-negative sequence. If there are negative values, handle them through translation transformation.

[0104] 4.2. Perform a first-order accumulation generation (1-AGO) on each original data sequence to obtain a new sequence where,

[0105] 4.3. Construct a grey differential equation, and use the least squares method to solve the parameters a and b. The differential equation is:

[0106]

[0107] 4.4. Gray differential equation-based prediction And restore the benchmark prediction values of each non-electricity indicator through inverse accumulation generation (IAGO).

[0108]

[0109] 4.5. Select the probability distribution based on the historical data of each non-electricity indicator.

[0110] 4.6. Use a random number generator to generate random samples that follow the probability distribution of the non-electricity indicators, generating N groups of random numbers;

[0111] 4.7. Monte Carlo simulation to generate the dynamic prediction normal distribution intervals of each non-electricity indicator where σ 2 is the historical residual variance.

[0112] 4.8. Accumulate the benchmark prediction values of each non-electricity indicator and the corresponding dynamic prediction intervals to obtain the dynamic prediction results of each non-electricity indicator:

[0113]

[0114] In the above formula, is the dynamic prediction value of the i-th non-electricity indicator highly correlated with carbon emissions in the (n + 1)-th year, is the benchmark prediction value of the i-th non-electricity indicator highly correlated with carbon emissions in the (n + 1)-th year,and is the dynamic prediction interval of the i-th non-electricity indicator highly correlated with carbon emissions.

[0115] 5. Based on the regional carbon emission model and the dynamic prediction results of each non-electricity indicator obtained in step 4, combined with carbon constraints, construct a regional power planning model. Among them,

[0116] The objective function of the regional power planning model includes:

[0117]

[0118] In the above formula, y1 and y2 are the regional thermal power generation and the proportion of regional electric energy in terminal energy consumption respectively, Q total is the regional electricity consumption, C is the predicted value of regional carbon emissions, is the predicted value of the i-th non-electricity indicator highly correlated with carbon emissions, E energy is the total regional terminal energy consumption, and κ is the electricity conversion standard coal coefficient;

[0119] The constraint conditions include:

[0120] Carbon constraint:

[0121] Cbase ≤C≤C target

[0122] In the above formula, C base , C target are the most basic carbon emissions of the region and the target value of regional carbon emissions respectively;

[0123] Thermal power generation constraint:

[0124] y min ≤y1≤y max

[0125] In the above formula, y min , y max are the regional benchmark load demand and the maximum thermal power generation of the region respectively;

[0126] Consumption ratio constraint:

[0127]

[0128] In the above formula, y 2,min , y 2,max are the minimum proportion of electric energy in the terminal energy consumption of the region and the maximum proportion of electric energy that can be achieved in the region limited by technology respectively, and Q total,min is the minimum electricity demand of the region.

[0129] 6. Solve the regional power planning model to obtain a regional power planning scheme including the regional thermal power generation and electricity consumption.

[0130] Taking the solution of the maximum value of the regional thermal power generation as an example, the specific steps are as follows:

[0131] (1) Analyze the elasticity coefficient in the regional carbon emission model. The per capita GDP, energy consumption intensity, urbanization rate, and the proportion of the added value of the secondary industry in GDP are positively correlated with carbon emissions, and their lower limit values are taken; the proportion of electric energy in the terminal energy consumption is negatively correlated with carbon emissions, and its upper limit value is taken; the carbon emissions take the upper limit value, and there is:

[0132]

[0133] In the above formula, β i is the error limit corresponding to the i-th non-electricity index, which is determined by Determine.

[0134] (2) Substitute the above parameters into the objective function to solve the maximum value of the regional thermal power generation max y1.

[0135] (3) Verify whether max y1 satisfies the thermal power generation constraint, and the maximum value of the thermal power generation y1 is:

[0136]

[0137] Example 2:

[0138] A regional power planning system under carbon constraint, as Figure 3 shown, includes a regional carbon emission model construction module, a non-electricity index dynamic prediction module, a regional power planning model construction module, and a regional power planning model solution module.

[0139] The regional carbon emission model construction module is used to construct the following regional carbon emission model based on regional historical carbon emissions, thermal power generation, the proportion of electricity in terminal energy consumption, and non-electricity index data highly related to high carbon emissions:

[0140]

[0141] Y1 = {y1(1), y1(2),..., y1(i),..., y1(n)}

[0142] Y2 = {y2(1), y2(2),..., y2(i),..., y2(n)}

[0143] In the above formula, C pre is the regional historical carbon emissions, is the historical data sequence of the i-th non-electricity index highly related to high carbon emissions. Y1 and Y2 are the regional thermal power generation sequence and the sequence of the proportion of electricity in terminal energy consumption respectively. y1(i) and y2(i) are the regional thermal power generation and the proportion of electricity in terminal energy consumption in the i-th historical year respectively. α i is the elasticity coefficient of the i-th non-electricity index highly related to high carbon emissions. α m+1 , α m+2 are the elasticity coefficients of regional thermal power generation and the proportion of electricity in terminal energy consumption respectively. α0 is a constant term. m and n are the number of non-electricity indices highly related to high carbon emissions and the number of historical years respectively.

[0144] The non-electricity index dynamic prediction module is used to dynamically predict the development trend of non-electricity indices highly related to high carbon emissions, including a grey prediction unit, a Monte Carlo model unit, and an accumulation unit.

[0145] The grey prediction unit is used to obtain the benchmark prediction values of each non-electricity index through grey prediction based on the historical data of non-electricity indices highly related to high carbon emissions. The specific implementation method includes:

[0146] 1.1 Pretreat the historical data of each non-electricity index highly related to high carbon emissions to ensure that the original data sequence is a non-negative sequence. If there are negative values, they are processed by translation transformation.

[0147] 1.2. Perform a single cumulative generation (1-AGO) on each original data sequence to obtain a new sequence Among them,

[0148] 1.3. Construct a grey differential equation, and use the least squares method to solve the parameters a and b. The differential equation is:

[0149]

[0150] 1.4. Predict based on the grey differential equation And restore the benchmark prediction values of each non-electricity index through inverse cumulative generation (IAGO)

[0151]

[0152] The Monte Carlo model unit is used to simulate and generate the dynamic prediction intervals of each non-electricity index through the Monte Carlo model based on the historical data of non-electricity indices highly correlated with carbon emissions. The specific implementation method includes:

[0153] 2.1. Select its probability distribution based on the historical data of each non-electricity index;

[0154] 2.2. Use a random number generator to generate random samples that follow the probability distribution of the non-electricity index, and generate N groups of random numbers;

[0155] 2.3. Monte Carlo simulation generates the dynamic prediction normal distribution intervals of each non-electricity index.

[0156] The accumulation unit is used to accumulate the benchmark prediction value of each non-electricity index with the corresponding dynamic prediction interval to obtain the dynamic prediction result of each non-electricity index:

[0157]

[0158] In the above formula, is the dynamic prediction value of the i-th non-electricity index highly correlated with carbon emissions in the (n + 1)-th year, is the benchmark prediction value of the i-th non-electricity index highly correlated with carbon emissions in the (n + 1)-th year, is the dynamic prediction interval of the i-th non-electricity index highly correlated with carbon emissions.

[0159] The regional power planning model construction module is used to construct a regional power planning model based on the regional carbon emission model and the dynamic prediction results of non-electricity indices, combined with carbon constraints. Among them, the objective function of the regional power planning model includes:

[0160]

[0161] In the above formula, y1 and y2 are the regional thermal power generation and the proportion of regional electric energy in terminal energy consumption respectively, Q total is the regional electricity consumption, C is the predicted value of regional carbon emissions, is the predicted value of the i-th non-electricity index highly correlated with carbon emissions, E energy is the total amount of regional terminal energy consumption, and κ is the conversion coefficient of electricity to standard coal;

[0162] The constraint conditions include:

[0163] Carbon constraint:

[0164] C base ≤C≤C target

[0165] In the above formula, C base and C target are the most basic carbon emissions and the regional carbon emissions target value respectively;

[0166] Thermal power generation constraint:

[0167] y min ≤y1≤y max

[0168] In the above formula, y min and y max are the regional benchmark load demand and the regional maximum thermal power generation respectively;

[0169] Consumption proportion constraint:

[0170]

[0171] In the above formula, y 2,min and y 2,max are the minimum proportion of electric energy in regional terminal energy consumption and the maximum proportion of electric energy in regional terminal energy consumption limited by technology respectively, Q total,min is the regional minimum electricity demand.

[0172] The regional power planning model solving module is used to solve the regional power planning model and obtain a regional power planning scheme including regional thermal power generation and electricity consumption.

Claims

1. A regional power planning method under carbon constraint, characterized in that the method includes: S1. Based on the regional historical carbon emissions, thermal power generation, the proportion of electricity in terminal energy consumption, and non-electricity index data related to high carbon emissions, construct a regional carbon emission model; dynamically predict the development trend of non-electricity indicators related to high carbon emissions; S2. Based on the regional carbon emission model and the dynamic prediction results of non-electricity indicators, combined with carbon constraints, construct a regional power planning model; S3. Solve the regional power planning model to obtain a regional power planning scheme including regional thermal power generation and electricity consumption.

2. The regional power planning method under carbon constraint according to claim 1, characterized in that in S1, the regional carbon emission model is: Y1 = {y1(1), y1(2),..., y1(i),..., y1(n)} Y2 = {y2(1), y2(2),..., y2(i),..., y2(n)} In the above formula, Cpre is the historical carbon emissions of the region, is the historical data series of the i-th non-electricity indicator highly correlated with carbon emissions. Y1 and Y2 are the regional thermal power generation series and the regional electricity consumption proportion in terminal energy consumption series respectively. y1(i) and y2(i) are the regional thermal power generation and the regional electricity consumption proportion in terminal energy consumption in the i-th historical year respectively. α i is the elasticity coefficient of the i-th non-electricity indicator highly correlated with carbon emissions. α m+1 and α m+2 are the elasticity coefficients of the regional thermal power generation and the regional electricity consumption proportion in terminal energy consumption respectively. α0 is the constant term. m and n are the number of non-electricity indicators highly correlated with carbon emissions and the number of historical years respectively.

3. The regional power planning method under carbon constraint according to claim 2, characterized in that in S2, the objective function of the regional power planning model includes: In the above formula, y1 and y2 are the regional thermal power generation and the proportion of regional electric energy in terminal energy consumption respectively, Qtotal is the regional electricity consumption, C is the predicted value of regional carbon emissions, is the predicted value of the i-th non-electricity index highly correlated with carbon emissions, E energy is the total amount of regional terminal energy consumption, and κ is the conversion coefficient of electricity to standard coal; The constraint conditions include: Carbon constraint: Cbase ≤ C ≤ Ctarget In the above formula, Cbase and Ctarget are the regional minimum carbon emissions and the regional carbon emission target value respectively; Thermal power generation constraint: ymin ≤ y1 ≤ ymax In the above formula, ymin and ymax are the regional benchmark load demand and the regional maximum thermal power generation respectively; Consumption proportion constraint: In the above formula, y2,min and y2,max are the regional minimum proportion of electricity in terminal energy consumption and the highest proportion of electricity in terminal energy consumption limited by technology respectively, and Qtotal,min is the regional minimum electricity demand.

4. The regional power planning method under carbon constraint according to claim 1, characterized in that in S1, dynamically predicting the development trend of non-electricity indicators related to high carbon emissions includes: S11. Based on the historical data of non-electricity indicators related to high carbon emissions, respectively obtain the benchmark prediction values of each non-electricity indicator through grey prediction, and generate the dynamic prediction intervals of each non-electricity indicator through Monte Carlo model simulation; S12. Accumulate the benchmark prediction values of each non-electricity indicator and the corresponding dynamic prediction intervals to obtain the dynamic prediction results of each non-electricity indicator: In the above formula, is the dynamic prediction value of the i-th non-electricity indicator highly related to high carbon emissions in the (n + 1)-th year, is the benchmark prediction value of the i-th non-electricity indicator highly related to high carbon emissions in the (n + 1)-th year, is the dynamic prediction interval of the i-th non-electricity indicator highly related to high carbon emissions.

5. The regional power planning method under carbon constraint according to claim 4, characterized in that in S11, generating the dynamic prediction intervals of each non-electricity indicator through Monte Carlo model simulation includes: S111. Based on the historical data of each non-electricity indicator, select its probability distribution; S112. Use a random number generator to generate random samples that follow the probability distribution of non-electricity indicators, and generate N groups of random numbers; S113. Monte Carlo simulation generates the dynamic prediction normal distribution intervals of each non-electricity indicator.

6. A regional power planning system under carbon constraint, characterized in that The system includes a regional carbon emission model construction module, a non-electricity index dynamic prediction module, a regional power planning model construction module, and a regional power planning model solution module; The regional carbon emission model construction module is used to construct a regional carbon emission model based on regional historical carbon emissions, thermal power generation, the proportion of electricity in terminal energy consumption, and non-electricity index data related to high carbon emissions; The non-electricity index dynamic prediction module is used to dynamically predict the development trend of non-electricity indexes related to high carbon emissions; The regional power planning model construction module is used to construct a regional power planning model based on the regional carbon emission model and the dynamic prediction results of non-electricity indexes, combined with carbon constraints; The regional power planning model solution module is used to solve the regional power planning model to obtain a regional power planning scheme including regional thermal power generation and electricity consumption.

7. The regional power planning system under carbon constraints according to claim 6, characterized in that The regional carbon emission model is: Y1 = {y1(1), y1(2),..., y1(i),..., y1(n)} Y2 = {y2(1), y2(2),..., y2(i),..., y2(n)} In the above formula, Cpre is the historical carbon emissions of the region, is the historical data series of the i-th non-electricity indicator highly correlated with carbon emissions. Y1 and Y2 are the regional thermal power generation series and the series of the proportion of regional electric energy in terminal energy consumption respectively. y1(i) and y2(i) are the regional thermal power generation and the proportion of regional electric energy in terminal energy consumption in the i-th historical year respectively. α i is the elasticity coefficient of the i-th non-electricity indicator highly correlated with carbon emissions. α m+1 and α m+2 are the elasticity coefficients of regional thermal power generation and the proportion of regional electric energy in terminal energy consumption respectively. α0 is the constant term. m and n are the number of non-electricity indicators highly correlated with carbon emissions and the number of historical years respectively.

8. The regional power planning system under carbon constraints according to claim 7, characterized in that The objective function of the regional power planning model includes: In the above formula, y1 and y2 are the regional thermal power generation and the proportion of regional electric energy in terminal energy consumption respectively, Qtotal is the regional electricity consumption, C is the predicted value of regional carbon emissions, is the predicted value of the i-th non-electricity index highly correlated with carbon emissions, E energy is the total regional terminal energy consumption, and κ is the coefficient of converting electricity into standard coal; The constraint conditions include: Carbon constraint: Cbase ≤ C ≤ Ctarget In the above formula, Cbase and Ctarget are the regional minimum carbon emissions and the regional carbon emission target value respectively; Thermal power generation constraint: ymin ≤ y1 ≤ ymax In the above formula, ymin and ymax are the regional reference load demand and the regional maximum thermal power generation respectively; Consumption proportion constraint: In the above formula, y2,min and y2,max are the regional minimum proportion of electricity in terminal energy consumption and the regional maximum proportion of electricity in terminal energy consumption limited by technology respectively, and Qtotal,min is the regional minimum electricity demand.

9. The regional power planning system under carbon constraints according to claim 6, characterized in that The non-electricity index dynamic prediction module includes a grey prediction unit, a Monte Carlo model unit, and an accumulation unit; The grey prediction unit is used to obtain the reference prediction values of each non-electricity index through grey prediction based on the historical data of non-electricity indexes related to high carbon emissions; The Monte Carlo model unit is used to simulate and generate the dynamic prediction intervals of each non-electricity index through the Monte Carlo model based on the historical data of non-electricity indexes related to high carbon emissions; The accumulation unit is used to accumulate the reference prediction values of each non-electricity index and the corresponding dynamic prediction intervals to obtain the dynamic prediction results of each non-electricity index: In the above formula, is the dynamic prediction value of the i-th non-electricity indicator highly related to high carbon emissions in the (n + 1)-th year, is the benchmark prediction value of the i-th non-electricity indicator highly related to high carbon emissions in the (n + 1)-th year, is the dynamic prediction interval of the i-th non-electricity indicator highly related to high carbon emissions.

10. The regional power planning system under carbon constraints according to claim 9, characterized in that The Monte Carlo model unit adopts the following strategy to generate the dynamic prediction intervals of each non-electricity index: A1. Based on the historical data of each non-electricity index, select its probability distribution; A2. Use a random number generator to generate random samples that follow the probability distribution of non-electricity indicators, and generate N groups of random numbers; A3. Use Monte Carlo simulation to generate the dynamic prediction normal distribution intervals of each non-electricity indicator.

Images